导读 本文是对发表于 NeurIPS 2023 论文 GenPose: Generative Category-level Object Pose Estimation via Diffusion Models 的解读。该论文由北京大学董豪超平面实验室完成,共同一作为计算机学院博士生张继耀、吴铭东。 类别级 6D 物体位姿估计是一个基础且重要的问题,在机器人、虚拟现实和增强现实等领域应用广泛。本文...
Breadcrumbs GenPose / README.mdTop File metadata and controls Preview Code Blame 183 lines (152 loc) · 7.19 KB Raw GenPose: Generative Category-level Object Pose Estimation via Diffusion Models The official Pytorch implementation of the NeurIPS 2023 paper, GenPose. Overview (I) A score-based...
Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {{ message }} Jiyao06 / GenPose Public Notifications You must be signed in to change notification settings Fork 5 Star 153 ...
Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {{ message }} Jiyao06 / GenPose Public Notifications You must be signed in to change notification settings Fork 5 Star 153 ...
2024.07.01:Omni6DPosehas been accepted by ECCV2024! 🎉 📆 TODO Release the Omni6DPose dataset. Release the Omni6DPose API. Release the GenPose++ and pretrained models. Release a convenient version of GenPose++ with SAM for the downstream tasks. ...
PoseGen: Learning to Generate 3D Human Pose Dataset with NeRF https://arxiv.org/abs/2312.14915 Mohsen Gholami, Rabab Ward, Z. Jane Wang University of British Columbia 本文提出了一种使用神经辐射场 (NeRF) 生成 3D 人体姿势数据集的端到端框架。 公共数据集在人体姿势和相机视角方面的多样性通常有限...
However, we all know that there have always been workarounds for vendors who needed the "marketing" story of the next generation without doing the heavy lifting of application redesign and rewrite.Naomi Lee BloomHuman Resource Outsourcing Today...
手部21个关键点检测,二维手势姿态,手势识别,pytorch,handpose. Contribute to studyHooligen/handpose_x development by creating an account on GitHub.
话本小说网免费提供zacpposegen作品集, zacpposegen全部小说以及zacpposegen新书在线全文阅读,提供给您最全面的zacpposegen小说集在线阅读服务,欢迎查看。
作者把该问题看作条件分布建模问题,提出了一种名为 GenPose 的方法,利用扩散模型来估计物体位姿的条件分布。该方法首先使用基于分数的扩散模型生成物体位姿的候选项。然后通过两步对候选项进行聚合:首先,通过似然估计筛选掉异常值,接着通过平均池化对剩余候选位姿进行聚合。为了避免在估计似然时需要繁琐的积分计算,研究...